{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas 进阶修炼 ｜早起Python\n",
    "<br>\n",
    "\n",
    "**本习题由公众号【早起Python & 可视化图鉴】 原创，转载及其他形式合作请与我们联系（微信号`sshs321`)，未经授权严禁搬运及二次创作，侵权必究！**\n",
    "\n",
    "\n",
    "\n",
    "本习题基于 `pandas` 版本 `1.1.3`，所有内容应当在 `Jupyter Notebook` 中执行以获得最佳效果。\n",
    "\n",
    "不同版本之间写法可能会有少许不同，如若碰到此情况，你应该学会如何自行检索解决。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 东京奥运会奖牌分析可视化\n",
    "\n",
    "\n",
    "<br>\n",
    "\n",
    "现在，让我们用一个简单的数据集来对之前的 pandas 操作。\n",
    "\n",
    "本次实战将使用两份东京奥运会数据进行。\n",
    "\n",
    "将以数据分析可视化为主题，涉及到几乎前面每个章节的内容。\n",
    "\n",
    "为了方便大家理解题意，我将会保留部分习题的运行结果。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初始化\n",
    "\n",
    "<br>\n",
    "\n",
    "该 `Notebook` 版本为**纯习题版**\n",
    "\n",
    "如果需要答案或者提示，可以微信搜索公众号「早起Python」获取！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据加载与预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 - 加载数据\n",
    "\n",
    "读取当前目录下\n",
    "- 东京奥运会奖牌数据.csv -> df1\n",
    "- 东京奥运会奖牌分日数据.csv -> df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "# 中文\n",
    "\n",
    "if sys.platform == 'darwin':\n",
    "    plt.rcParams['font.sans-serif']=['Songti SC'] \n",
    "else:\n",
    "    plt.rcParams['font.sans-serif']=['SimHei'] \n",
    "    \n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "df1 = pd.read_csv('./东京奥运会奖牌数据.csv')\n",
    "df2 = pd.read_csv('./东京奥运会奖牌分日数据.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif']=['SimHei'] \n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2- 修改列名\n",
    "\n",
    "<br>\n",
    "\n",
    "将原 `df1` 列名 `Unnamed: 2`、`Unnamed: 3`、`Unnamed: 4` 修改为 `金牌数`、`银牌数`、`铜牌数`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.rename(columns= {\n",
    "    'Unnamed: 2':'金牌数',\n",
    "    'Unnamed: 3':'银牌数',\n",
    "    'Unnamed: 4':'铜牌数'}, inplace = True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 - 数据类型查看\n",
    "\n",
    "查看 df2 的数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1083 entries, 0 to 1082\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   获奖时间    1083 non-null   object\n",
      " 1   奖牌类型    1083 non-null   int64 \n",
      " 2   运动员     1083 non-null   object\n",
      " 3   国家id    1083 non-null   int64 \n",
      " 4   运动类别    1083 non-null   object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 42.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 - 类型修改\n",
    "\n",
    "将 df2 的获奖时间修改为 时间格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.astype({'获奖时间':np.Datetime64})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 - 数据排序\n",
    "\n",
    "将 df2 按照获奖时间升序排列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>获奖时间</th>\n",
       "      <th>奖牌类型</th>\n",
       "      <th>运动员</th>\n",
       "      <th>国家id</th>\n",
       "      <th>运动类别</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>2021-07-24 00:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>亚力杭德拉·瓦伦西亚/路易斯·阿尔瓦雷斯</td>\n",
       "      <td>156</td>\n",
       "      <td>射箭</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>2021-07-24 00:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>史蒂夫·维勒等</td>\n",
       "      <td>1</td>\n",
       "      <td>射箭</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>2021-07-24 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>安山/金智德</td>\n",
       "      <td>106</td>\n",
       "      <td>射箭</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>2021-07-24 07:30:00</td>\n",
       "      <td>1</td>\n",
       "      <td>杨倩</td>\n",
       "      <td>26</td>\n",
       "      <td>射击</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>2021-07-24 07:30:00</td>\n",
       "      <td>3</td>\n",
       "      <td>妮娜·克里斯汀</td>\n",
       "      <td>149</td>\n",
       "      <td>射击</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-08-08 14:49:00</td>\n",
       "      <td>1</td>\n",
       "      <td>巴霍迪尔·贾洛洛夫</td>\n",
       "      <td>15</td>\n",
       "      <td>拳击</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-08-08 15:34:00</td>\n",
       "      <td>2</td>\n",
       "      <td>俄奥委会女子手球队</td>\n",
       "      <td>23062</td>\n",
       "      <td>手球</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-08-08 15:34:00</td>\n",
       "      <td>1</td>\n",
       "      <td>法国女子手球队</td>\n",
       "      <td>144</td>\n",
       "      <td>手球</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-08-08 16:44:00</td>\n",
       "      <td>2</td>\n",
       "      <td>希腊男子水球队</td>\n",
       "      <td>171</td>\n",
       "      <td>水球</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-08-08 16:44:00</td>\n",
       "      <td>1</td>\n",
       "      <td>塞尔维亚男子水球队</td>\n",
       "      <td>107</td>\n",
       "      <td>水球</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1083 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    获奖时间  奖牌类型                   运动员   国家id 运动类别\n",
       "1082 2021-07-24 00:00:00     3  亚力杭德拉·瓦伦西亚/路易斯·阿尔瓦雷斯    156   射箭\n",
       "1081 2021-07-24 00:00:00     2               史蒂夫·维勒等      1   射箭\n",
       "1080 2021-07-24 00:00:00     1                安山/金智德    106   射箭\n",
       "1077 2021-07-24 07:30:00     1                    杨倩     26   射击\n",
       "1079 2021-07-24 07:30:00     3               妮娜·克里斯汀    149   射击\n",
       "...                  ...   ...                   ...    ...  ...\n",
       "4    2021-08-08 14:49:00     1             巴霍迪尔·贾洛洛夫     15   拳击\n",
       "3    2021-08-08 15:34:00     2             俄奥委会女子手球队  23062   手球\n",
       "2    2021-08-08 15:34:00     1               法国女子手球队    144   手球\n",
       "1    2021-08-08 16:44:00     2               希腊男子水球队    171   水球\n",
       "0    2021-08-08 16:44:00     1             塞尔维亚男子水球队    107   水球\n",
       "\n",
       "[1083 rows x 5 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sort_values(['获奖时间'], ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6 - 匹配修改\n",
    "\n",
    "给 df2 新增一列国家，总奖牌数，值根据 国家id 与 df1 匹配\n",
    "\n",
    "注意：原始数据可能有一点问题，射击队杨倩应该是东京首金"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>获奖时间</th>\n",
       "      <th>奖牌类型</th>\n",
       "      <th>运动员</th>\n",
       "      <th>国家id</th>\n",
       "      <th>运动类别</th>\n",
       "      <th>总奖牌数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-08-08 16:44:00</td>\n",
       "      <td>1</td>\n",
       "      <td>塞尔维亚男子水球队</td>\n",
       "      <td>107</td>\n",
       "      <td>水球</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-08-08 16:44:00</td>\n",
       "      <td>2</td>\n",
       "      <td>希腊男子水球队</td>\n",
       "      <td>171</td>\n",
       "      <td>水球</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-08-08 15:34:00</td>\n",
       "      <td>1</td>\n",
       "      <td>法国女子手球队</td>\n",
       "      <td>144</td>\n",
       "      <td>手球</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-08-08 15:34:00</td>\n",
       "      <td>2</td>\n",
       "      <td>俄奥委会女子手球队</td>\n",
       "      <td>23062</td>\n",
       "      <td>手球</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-08-08 14:49:00</td>\n",
       "      <td>1</td>\n",
       "      <td>巴霍迪尔·贾洛洛夫</td>\n",
       "      <td>15</td>\n",
       "      <td>拳击</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>2021-07-24 07:30:00</td>\n",
       "      <td>2</td>\n",
       "      <td>阿纳斯塔西娅·加拉西娜</td>\n",
       "      <td>23062</td>\n",
       "      <td>射击</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>2021-07-24 07:30:00</td>\n",
       "      <td>3</td>\n",
       "      <td>妮娜·克里斯汀</td>\n",
       "      <td>149</td>\n",
       "      <td>射击</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>2021-07-24 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>安山/金智德</td>\n",
       "      <td>106</td>\n",
       "      <td>射箭</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>2021-07-24 00:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>史蒂夫·维勒等</td>\n",
       "      <td>1</td>\n",
       "      <td>射箭</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>2021-07-24 00:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>亚力杭德拉·瓦伦西亚/路易斯·阿尔瓦雷斯</td>\n",
       "      <td>156</td>\n",
       "      <td>射箭</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1083 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    获奖时间  奖牌类型                   运动员   国家id 运动类别  总奖牌数\n",
       "0    2021-08-08 16:44:00     1             塞尔维亚男子水球队    107   水球     9\n",
       "1    2021-08-08 16:44:00     2               希腊男子水球队    171   水球     4\n",
       "2    2021-08-08 15:34:00     1               法国女子手球队    144   手球    33\n",
       "3    2021-08-08 15:34:00     2             俄奥委会女子手球队  23062   手球    71\n",
       "4    2021-08-08 14:49:00     1             巴霍迪尔·贾洛洛夫     15   拳击     5\n",
       "...                  ...   ...                   ...    ...  ...   ...\n",
       "1078 2021-07-24 07:30:00     2           阿纳斯塔西娅·加拉西娜  23062   射击    71\n",
       "1079 2021-07-24 07:30:00     3               妮娜·克里斯汀    149   射击    13\n",
       "1080 2021-07-24 00:00:00     1                安山/金智德    106   射箭    20\n",
       "1081 2021-07-24 00:00:00     2               史蒂夫·维勒等      1   射箭    36\n",
       "1082 2021-07-24 00:00:00     3  亚力杭德拉·瓦伦西亚/路易斯·阿尔瓦雷斯    156   射箭     4\n",
       "\n",
       "[1083 rows x 6 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['总奖牌数'] = df2.国家id.apply(lambda x: df1[df1.国家id == x][['金牌数','银牌数','铜牌数']].sum(axis=1).iloc[0])\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.merge(left=df2, right=df1, how='left', on='国家id')\n",
    "\n",
    "df2['总奖牌数'] = df3.金牌数 + df3.银牌数 + df3.铜牌数\n",
    "df2['国家'] = df3.国家奥委会"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7 - 分组统计\n",
    "\n",
    "通过对 df2 的国家进行分组统计，计算每个国家的奖牌总数（也就是出现次数）\n",
    "\n",
    "并查看奖牌数前5名，结果可以用 df1 进行验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "美国     113\n",
       "中国      88\n",
       "ROC     71\n",
       "英国      65\n",
       "日本      58\n",
       "Name: 国家, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.国家.value_counts().sort_values(ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "国家\n",
       "美国     113\n",
       "中国      88\n",
       "ROC     71\n",
       "英国      65\n",
       "日本      58\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('国家').size().sort_values(ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = df1.loc[:4,['国家奥委会','总分']]\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.axis('equal')\n",
    "plt.pie(temp.总分, labels=temp.国家奥委会,autopct=lambda x: '%0.2f%%'%(x))\n",
    "plt.legend(temp.国家奥委会)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['美国', '中国', '日本', '英国', 'ROC', '澳大利亚'], dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[:5,'国家奥委会'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8 - 数据统计\n",
    "\n",
    "通过 df2 计算获得奖牌最多的运动员\n",
    "\n",
    "注意：仅统计单人项目"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "塞勒博·德雷赛尔     3\n",
       "张雨霏          2\n",
       "罗伯特·芬克       2\n",
       "米哈伊罗·罗曼丘克    2\n",
       "大桥悠依         2\n",
       "Name: 运动员, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.运动员.value_counts().sort_values(ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>运动员</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>塞勒博·德雷赛尔</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>张雨霏</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>肖若腾</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>大卫·尼卡</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>桥本大辉</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          运动员  size\n",
       "271  塞勒博·德雷赛尔     3\n",
       "422       张雨霏     2\n",
       "756       肖若腾     2\n",
       "292     大卫·尼卡     2\n",
       "557      桥本大辉     2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(by='运动员', as_index=False).运动员.size().sort_values('size', ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9 - 数据查看\n",
    "\n",
    "查看乒乓球项目的全部信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>获奖时间</th>\n",
       "      <th>奖牌类型</th>\n",
       "      <th>运动员</th>\n",
       "      <th>国家id</th>\n",
       "      <th>运动类别</th>\n",
       "      <th>总奖牌数</th>\n",
       "      <th>国家</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2021-08-06 20:42:00</td>\n",
       "      <td>2</td>\n",
       "      <td>派翠克·法兰兹卡等</td>\n",
       "      <td>2</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>37</td>\n",
       "      <td>德国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>2021-08-06 20:40:00</td>\n",
       "      <td>1</td>\n",
       "      <td>马龙/许昕/樊振东</td>\n",
       "      <td>26</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>88</td>\n",
       "      <td>中国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>2021-08-06 12:42:00</td>\n",
       "      <td>3</td>\n",
       "      <td>日本</td>\n",
       "      <td>18</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>58</td>\n",
       "      <td>日本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>2021-08-05 20:41:00</td>\n",
       "      <td>2</td>\n",
       "      <td>伊藤美诚/石川佳纯/平野美宇</td>\n",
       "      <td>18</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>58</td>\n",
       "      <td>日本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>2021-08-05 20:34:00</td>\n",
       "      <td>1</td>\n",
       "      <td>陈梦/王曼昱/孙颖莎</td>\n",
       "      <td>26</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>88</td>\n",
       "      <td>中国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>2021-08-05 12:39:00</td>\n",
       "      <td>3</td>\n",
       "      <td>杜凯琹/苏慧音/李皓晴</td>\n",
       "      <td>38</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>6</td>\n",
       "      <td>中国香港</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>2021-07-30 21:29:00</td>\n",
       "      <td>1</td>\n",
       "      <td>马龙</td>\n",
       "      <td>26</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>88</td>\n",
       "      <td>中国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>2021-07-30 21:29:00</td>\n",
       "      <td>2</td>\n",
       "      <td>樊振东</td>\n",
       "      <td>26</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>88</td>\n",
       "      <td>中国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>2021-07-30 20:16:00</td>\n",
       "      <td>3</td>\n",
       "      <td>迪米特里·奥恰洛夫</td>\n",
       "      <td>2</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>37</td>\n",
       "      <td>德国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>2021-07-29 21:10:00</td>\n",
       "      <td>1</td>\n",
       "      <td>陈梦</td>\n",
       "      <td>26</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>88</td>\n",
       "      <td>中国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>2021-07-29 21:10:00</td>\n",
       "      <td>2</td>\n",
       "      <td>孙颖莎</td>\n",
       "      <td>26</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>88</td>\n",
       "      <td>中国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>2021-07-29 20:02:00</td>\n",
       "      <td>3</td>\n",
       "      <td>伊藤美诚</td>\n",
       "      <td>18</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>58</td>\n",
       "      <td>日本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>2021-07-26 22:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>水谷隼/伊藤美诚</td>\n",
       "      <td>18</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>58</td>\n",
       "      <td>日本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>2021-07-26 22:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>许昕/刘诗雯</td>\n",
       "      <td>26</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>88</td>\n",
       "      <td>中国</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>2021-07-26 00:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>林昀儒/郑怡静</td>\n",
       "      <td>31</td>\n",
       "      <td>乒乓球</td>\n",
       "      <td>12</td>\n",
       "      <td>中华台北</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   获奖时间  奖牌类型             运动员  国家id 运动类别  总奖牌数    国家\n",
       "164 2021-08-06 20:42:00     2       派翠克·法兰兹卡等     2  乒乓球    37    德国\n",
       "165 2021-08-06 20:40:00     1       马龙/许昕/樊振东    26  乒乓球    88    中国\n",
       "209 2021-08-06 12:42:00     3              日本    18  乒乓球    58    日本\n",
       "223 2021-08-05 20:41:00     2  伊藤美诚/石川佳纯/平野美宇    18  乒乓球    58    日本\n",
       "227 2021-08-05 20:34:00     1      陈梦/王曼昱/孙颖莎    26  乒乓球    88    中国\n",
       "283 2021-08-05 12:39:00     3     杜凯琹/苏慧音/李皓晴    38  乒乓球     6  中国香港\n",
       "662 2021-07-30 21:29:00     1              马龙    26  乒乓球    88    中国\n",
       "663 2021-07-30 21:29:00     2             樊振东    26  乒乓球    88    中国\n",
       "664 2021-07-30 20:16:00     3       迪米特里·奥恰洛夫     2  乒乓球    37    德国\n",
       "727 2021-07-29 21:10:00     1              陈梦    26  乒乓球    88    中国\n",
       "728 2021-07-29 21:10:00     2             孙颖莎    26  乒乓球    88    中国\n",
       "734 2021-07-29 20:02:00     3            伊藤美诚    18  乒乓球    58    日本\n",
       "922 2021-07-26 22:00:00     1        水谷隼/伊藤美诚    18  乒乓球    58    日本\n",
       "923 2021-07-26 22:00:00     2          许昕/刘诗雯    26  乒乓球    88    中国\n",
       "986 2021-07-26 00:00:00     3         林昀儒/郑怡静    31  乒乓球    12  中华台北"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.运动类别 == '乒乓球']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10 - 数据透视\n",
    "\n",
    "查看各国在不同项目上的获奖牌情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>运动类别</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ROC</th>\n",
       "      <th>三人篮球</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>击剑</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>古典式摔跤</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>场地自行车赛</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>射击</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">韩国</th>\n",
       "      <th>竞技体操</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>羽毛球</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>跆拳道</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">马来西亚</th>\n",
       "      <th>场地自行车赛</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>羽毛球</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>516 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             size\n",
       "国家   运动类别        \n",
       "ROC  三人篮球       2\n",
       "     击剑         8\n",
       "     古典式摔跤      3\n",
       "     场地自行车赛     2\n",
       "     射击         8\n",
       "...           ...\n",
       "韩国   竞技体操       2\n",
       "     羽毛球        1\n",
       "     跆拳道        3\n",
       "马来西亚 场地自行车赛     1\n",
       "     羽毛球        1\n",
       "\n",
       "[516 rows x 1 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(['国家','运动类别'],as_index=False).size().set_index(['国家','运动类别'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>奖牌类型</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>运动类别</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">ROC</th>\n",
       "      <th>三人篮球</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>击剑</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>古典式摔跤</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>场地自行车赛</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>射击</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">韩国</th>\n",
       "      <th>竞技体操</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>羽毛球</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>跆拳道</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">马来西亚</th>\n",
       "      <th>场地自行车赛</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>羽毛球</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>516 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             奖牌类型\n",
       "国家   运动类别        \n",
       "ROC  三人篮球       2\n",
       "     击剑         8\n",
       "     古典式摔跤      3\n",
       "     场地自行车赛     2\n",
       "     射击         8\n",
       "...           ...\n",
       "韩国   竞技体操       2\n",
       "     羽毛球        1\n",
       "     跆拳道        3\n",
       "马来西亚 场地自行车赛     1\n",
       "     羽毛球        1\n",
       "\n",
       "[516 rows x 1 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.pivot_table(index=['国家','运动类别'], values='奖牌类型', aggfunc='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11 - 数据查询\n",
    "\n",
    "在上一题的基础上，查询中国队的获奖牌详情"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>运动类别</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"21\" valign=\"top\">中国</th>\n",
       "      <th>三人篮球</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>举重</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乒乓球</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>击剑</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>古典式摔跤</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>场地自行车赛</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>射击</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>帆船</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>拳击</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游泳</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>田径</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>空手道</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>竞技体操</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>羽毛球</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>自由式摔跤</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>花样游泳</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>赛艇</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>跆拳道</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>跳水</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>蹦床体操</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>静水皮划艇</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           size\n",
       "国家 运动类别        \n",
       "中国 三人篮球       1\n",
       "   举重         8\n",
       "   乒乓球        7\n",
       "   击剑         1\n",
       "   古典式摔跤      1\n",
       "   场地自行车赛     1\n",
       "   射击        11\n",
       "   帆船         2\n",
       "   拳击         2\n",
       "   游泳         6\n",
       "   田径         5\n",
       "   空手道        2\n",
       "   竞技体操       8\n",
       "   羽毛球        6\n",
       "   自由式摔跤      3\n",
       "   花样游泳       2\n",
       "   赛艇         3\n",
       "   跆拳道        1\n",
       "   跳水        12\n",
       "   蹦床体操       3\n",
       "   静水皮划艇      3"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(['国家','运动类别'],as_index=False).size().set_index(['国家','运动类别']).query('国家 == [\"中国\"]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>奖牌类型</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th>运动类别</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"21\" valign=\"top\">中国</th>\n",
       "      <th>三人篮球</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>举重</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乒乓球</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>击剑</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>古典式摔跤</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>场地自行车赛</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>射击</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>帆船</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>拳击</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游泳</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>田径</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>空手道</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>竞技体操</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>羽毛球</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>自由式摔跤</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>花样游泳</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>赛艇</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>跆拳道</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>跳水</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>蹦床体操</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>静水皮划艇</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           奖牌类型\n",
       "国家 运动类别        \n",
       "中国 三人篮球       1\n",
       "   举重         8\n",
       "   乒乓球        7\n",
       "   击剑         1\n",
       "   古典式摔跤      1\n",
       "   场地自行车赛     1\n",
       "   射击        11\n",
       "   帆船         2\n",
       "   拳击         2\n",
       "   游泳         6\n",
       "   田径         5\n",
       "   空手道        2\n",
       "   竞技体操       8\n",
       "   羽毛球        6\n",
       "   自由式摔跤      3\n",
       "   花样游泳       2\n",
       "   赛艇         3\n",
       "   跆拳道        1\n",
       "   跳水        12\n",
       "   蹦床体操       3\n",
       "   静水皮划艇      3"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.国家 == '中国'].pivot_table(index=['国家','运动类别'], values='奖牌类型', aggfunc=np.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12 - 个性化查看\n",
    "\n",
    "在数据框中根据奖牌数量进行可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table id=\"T_1570f_\">\n",
       "  <thead>\n",
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       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >奖牌类型</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"21\">中国</th>\n",
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       "      <td id=\"T_1570f_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row1\" class=\"row_heading level1 row1\" >举重</th>\n",
       "      <td id=\"T_1570f_row1_col0\" class=\"data row1 col0\" >8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row2\" class=\"row_heading level1 row2\" >乒乓球</th>\n",
       "      <td id=\"T_1570f_row2_col0\" class=\"data row2 col0\" >7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row3\" class=\"row_heading level1 row3\" >击剑</th>\n",
       "      <td id=\"T_1570f_row3_col0\" class=\"data row3 col0\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row4\" class=\"row_heading level1 row4\" >古典式摔跤</th>\n",
       "      <td id=\"T_1570f_row4_col0\" class=\"data row4 col0\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row5\" class=\"row_heading level1 row5\" >场地自行车赛</th>\n",
       "      <td id=\"T_1570f_row5_col0\" class=\"data row5 col0\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row6\" class=\"row_heading level1 row6\" >射击</th>\n",
       "      <td id=\"T_1570f_row6_col0\" class=\"data row6 col0\" >11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row7\" class=\"row_heading level1 row7\" >帆船</th>\n",
       "      <td id=\"T_1570f_row7_col0\" class=\"data row7 col0\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row8\" class=\"row_heading level1 row8\" >拳击</th>\n",
       "      <td id=\"T_1570f_row8_col0\" class=\"data row8 col0\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row9\" class=\"row_heading level1 row9\" >游泳</th>\n",
       "      <td id=\"T_1570f_row9_col0\" class=\"data row9 col0\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row10\" class=\"row_heading level1 row10\" >田径</th>\n",
       "      <td id=\"T_1570f_row10_col0\" class=\"data row10 col0\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row11\" class=\"row_heading level1 row11\" >空手道</th>\n",
       "      <td id=\"T_1570f_row11_col0\" class=\"data row11 col0\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row12\" class=\"row_heading level1 row12\" >竞技体操</th>\n",
       "      <td id=\"T_1570f_row12_col0\" class=\"data row12 col0\" >8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row13\" class=\"row_heading level1 row13\" >羽毛球</th>\n",
       "      <td id=\"T_1570f_row13_col0\" class=\"data row13 col0\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row14\" class=\"row_heading level1 row14\" >自由式摔跤</th>\n",
       "      <td id=\"T_1570f_row14_col0\" class=\"data row14 col0\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row15\" class=\"row_heading level1 row15\" >花样游泳</th>\n",
       "      <td id=\"T_1570f_row15_col0\" class=\"data row15 col0\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row16\" class=\"row_heading level1 row16\" >赛艇</th>\n",
       "      <td id=\"T_1570f_row16_col0\" class=\"data row16 col0\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row17\" class=\"row_heading level1 row17\" >跆拳道</th>\n",
       "      <td id=\"T_1570f_row17_col0\" class=\"data row17 col0\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row18\" class=\"row_heading level1 row18\" >跳水</th>\n",
       "      <td id=\"T_1570f_row18_col0\" class=\"data row18 col0\" >12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row19\" class=\"row_heading level1 row19\" >蹦床体操</th>\n",
       "      <td id=\"T_1570f_row19_col0\" class=\"data row19 col0\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1570f_level1_row20\" class=\"row_heading level1 row20\" >静水皮划艇</th>\n",
       "      <td id=\"T_1570f_row20_col0\" class=\"data row20 col0\" >3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2411b978c70>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.国家 == '中国'].pivot_table(index=['国家','运动类别'], values='奖牌类型', aggfunc=np.size).style.bar(color='skyblue', subset='奖牌类型')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = df2[df2.国家 == '中国'].pivot_table(index=['国家','运动类别'], columns=df2.奖牌类型.map({1:'金牌',2:'银牌',3:'铜牌'}), values='奖牌类型', aggfunc=np.size).fillna(0)\n",
    "\n",
    "ax = temp.plot.bar(figsize=(16,8), y=['金牌','银牌','铜牌'], stacked=True, width=0.8, alpha=0.7)\n",
    "ax.set_xticklabels(list(map(lambda x: x[1], temp.index)), fontsize=13)\n",
    "plt.title('中国各项运动奖牌数', fontsize=\"16\")\n",
    "\n",
    "x = 0\n",
    "for k, r in temp.iterrows():\n",
    "    if (r.金牌 > 0):\n",
    "        ax.text(x-0.1, r.金牌 /2, str(int(r.金牌)))\n",
    "    if (r.银牌 > 0):\n",
    "        ax.text(x-0.1, r.金牌 + r.银牌 /2, str(int(r.银牌)))\n",
    "    if (r.铜牌 > 0):\n",
    "        ax.text(x-0.1, r.金牌 + r.银牌 + r.铜牌/2, str(int(r.铜牌)))\n",
    "    x+=1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "ord() expected string of length 1, but tuple found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_98656/1985076982.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtemp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mord\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: ord() expected string of length 1, but tuple found"
     ]
    }
   ],
   "source": [
    "x = 0\n",
    "for k, r in temp.iterrows():\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13 - 数据格式化\n",
    "\n",
    "将 df2 的获奖时间格式化为 x月x日"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df2[:5].style.format({\"获奖时间\": '%Y-%M-%d'})\n",
    "df2.获奖时间 = df2.获奖时间.apply(lambda x: x.strftime('%m-%d') )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14 - 分组统计\n",
    "\n",
    "查看每天产生奖牌的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "获奖时间\n",
       "07-24     35\n",
       "07-25     58\n",
       "07-26     68\n",
       "07-27     71\n",
       "07-28     71\n",
       "07-29     53\n",
       "07-30     65\n",
       "07-31     70\n",
       "08-01     76\n",
       "08-02     64\n",
       "08-03     81\n",
       "08-04     59\n",
       "08-05     95\n",
       "08-06     80\n",
       "08-07    103\n",
       "08-08     34\n",
       "Name: 国家, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('获奖时间').国家.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15 - 数据透视\n",
    "\n",
    "查看不同项目在不同国家的分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>奖牌类型</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>运动类别</th>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">七人制橄榄球</th>\n",
       "      <th>斐济</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新西兰</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>阿根廷</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>三人篮球</th>\n",
       "      <th>ROC</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">高尔夫</th>\n",
       "      <th>中华台北</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>斯洛伐克</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新西兰</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>516 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             奖牌类型\n",
       "运动类别   国家        \n",
       "七人制橄榄球 斐济       2\n",
       "       新西兰      2\n",
       "       法国       1\n",
       "       阿根廷      1\n",
       "三人篮球   ROC      2\n",
       "...           ...\n",
       "高尔夫    中华台北     1\n",
       "       斯洛伐克     1\n",
       "       新西兰      1\n",
       "       日本       1\n",
       "       美国       2\n",
       "\n",
       "[516 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.pivot_table(index=['运动类别','国家'], values='奖牌类型', aggfunc=np.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 16 - 数据计算\n",
    "\n",
    "计算中国每日总奖牌数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>奖牌类型</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>获奖时间</th>\n",
       "      <th>国家</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>07-24</th>\n",
       "      <th>中国</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-25</th>\n",
       "      <th>中国</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-26</th>\n",
       "      <th>中国</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-27</th>\n",
       "      <th>中国</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-28</th>\n",
       "      <th>中国</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-29</th>\n",
       "      <th>中国</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-30</th>\n",
       "      <th>中国</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-31</th>\n",
       "      <th>中国</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-01</th>\n",
       "      <th>中国</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-02</th>\n",
       "      <th>中国</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-03</th>\n",
       "      <th>中国</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-04</th>\n",
       "      <th>中国</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-05</th>\n",
       "      <th>中国</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-06</th>\n",
       "      <th>中国</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-07</th>\n",
       "      <th>中国</th>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-08</th>\n",
       "      <th>中国</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          奖牌类型\n",
       "获奖时间  国家      \n",
       "07-24 中国     4\n",
       "07-25 中国    11\n",
       "07-26 中国    18\n",
       "07-27 中国    21\n",
       "07-28 中国    27\n",
       "07-29 中国    31\n",
       "07-30 中国    40\n",
       "07-31 中国    46\n",
       "08-01 中国    51\n",
       "08-02 中国    62\n",
       "08-03 中国    69\n",
       "08-04 中国    70\n",
       "08-05 中国    74\n",
       "08-06 中国    79\n",
       "08-07 中国    87\n",
       "08-08 中国    88"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df2[df2.国家 == '中国'], index=['获奖时间','国家'], values='奖牌类型',aggfunc=np.size).cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 20013 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 22269 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 38431 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 33719 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 22870 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 24773 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 20917 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 27599 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 22825 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 26102 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 38388 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 29260 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 25968 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 31867 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 22411 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 33719 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 22870 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 26102 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 38388 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 29260 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 25968 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 20013 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 22269 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 38431 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 24773 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 20917 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 27599 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 22825 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 31867 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 22411 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = pd.pivot_table(df2[df2.国家 == '中国'], index=['获奖时间','国家'], values='奖牌类型',aggfunc=np.size).reset_index()\n",
    "temp.drop(columns='国家', inplace=True)\n",
    "temp.set_index('获奖时间', inplace=True)\n",
    "\n",
    "ax = temp.plot.bar(figsize=(10,6), width=0.8,  alpha=0.9, ylabel=\"奖牌数\")\n",
    "plt.title('中国队获奖情况/每天')\n",
    "#plt.legend('fdsafd')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 20013 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 22269 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 32047 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 35745 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 22870 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 29260 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 25968 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 33719 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 26102 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 38388 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 33719 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 22870 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 26102 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 38388 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 29260 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 25968 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 20013 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 22269 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 32047 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 35745 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = pd.pivot_table(df2[df2.国家 == '中国'], index=['获奖时间','国家'], values='奖牌类型',aggfunc=np.size).cumsum().reset_index()\n",
    "temp.drop(columns='国家', inplace=True)\n",
    "temp.set_index('获奖时间', inplace=True)\n",
    "ax = temp.plot(kind='line', figsize=(12,6), ylabel='奖牌数')\n",
    "ax.legend(['奖牌累计'])\n",
    "\n",
    "plt.scatter(temp.index, temp['奖牌类型'])\n",
    "plt.xticks(temp.index, temp.index, fontsize=12) # 指定 x坐标\n",
    "\n",
    "plt.title('中国累计奖牌数', fontsize=16)\n",
    "\n",
    "for x, y in zip(temp.index, temp.奖牌类型):\n",
    "    plt.text(x, y + 2, str(y),fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>奖牌类型</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>获奖时间</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>07-24</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-25</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-26</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-27</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-28</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-29</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-30</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-31</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-01</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-02</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-03</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-04</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-05</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-06</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-07</th>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-08</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       奖牌类型\n",
       "获奖时间       \n",
       "07-24     4\n",
       "07-25    11\n",
       "07-26    18\n",
       "07-27    21\n",
       "07-28    27\n",
       "07-29    31\n",
       "07-30    40\n",
       "07-31    46\n",
       "08-01    51\n",
       "08-02    62\n",
       "08-03    69\n",
       "08-04    70\n",
       "08-05    74\n",
       "08-06    79\n",
       "08-07    87\n",
       "08-08    88"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>获奖时间</th>\n",
       "      <th>国家</th>\n",
       "      <th>奖牌类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>07-24</td>\n",
       "      <td>中国</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>07-25</td>\n",
       "      <td>中国</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>07-26</td>\n",
       "      <td>中国</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>07-27</td>\n",
       "      <td>中国</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>07-28</td>\n",
       "      <td>中国</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>07-29</td>\n",
       "      <td>中国</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>07-30</td>\n",
       "      <td>中国</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>07-31</td>\n",
       "      <td>中国</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>08-01</td>\n",
       "      <td>中国</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>08-02</td>\n",
       "      <td>中国</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>08-03</td>\n",
       "      <td>中国</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>08-04</td>\n",
       "      <td>中国</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>08-05</td>\n",
       "      <td>中国</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>08-06</td>\n",
       "      <td>中国</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>08-07</td>\n",
       "      <td>中国</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>08-08</td>\n",
       "      <td>中国</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     获奖时间  国家  奖牌类型\n",
       "0   07-24  中国     4\n",
       "1   07-25  中国     7\n",
       "2   07-26  中国     7\n",
       "3   07-27  中国     3\n",
       "4   07-28  中国     6\n",
       "5   07-29  中国     4\n",
       "6   07-30  中国     9\n",
       "7   07-31  中国     6\n",
       "8   08-01  中国     5\n",
       "9   08-02  中国    11\n",
       "10  08-03  中国     7\n",
       "11  08-04  中国     1\n",
       "12  08-05  中国     4\n",
       "13  08-06  中国     5\n",
       "14  08-07  中国     8\n",
       "15  08-08  中国     1"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pd.pivot_table(df2[df2.国家 == '中国'], index=['获奖时间','国家'], values='奖牌类型',aggfunc=np.size).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAGA4hFUAAAAAgOEQVgEAAAAAhkNYBQAAAAAYDmEVAAAAAGA4hFUAAAAAgOEQVgEAAAAAhkNYBQAAAAAYDmEVAAAAAGA4hFUAAAAAgOEQVgEAAAAAhkNYBQAAAAAYDmEVAAAAAGA4hFUAAAAAgOEQVgEAAAAAhkNYBQAAAAAYDmEVAAAAAGA4hFUAAAAAgOEQVgEAAAAAhkNYBQAAAAAYDmEVAAAAAGA4hFUAAAAAgOEQVgEAAAAAhkNYBQAAAAAYDmEVAAAAAGA4hFUAAAAAgOEQVgEAAAAAhkNYBQAAAAAYDmEVAAAAAGA4hFUAAAAAgOEQVgEAAAAAhkNYBQAAAAAYjsUbB50xY4aOHj2qli1bql+/ft4oAQAAAABgYCU+s/rjjz/K4XAoJiZGaWlpSk5OLukSAAAAAAAGZ3I6nc6SPOCcOXPUokULtWrVSlu3btW5c+fUqVMn1/NxcXGKi4uTJE2aNKkkSwMAAAAAGESJz6zm5OQoIiJCkhQaGqozZ864PR8dHa1JkyYZMqi++uqr3i7BY+jN9/hrXxK9+Sp/7c1f+5LozVfRm+/x174kevNVvtJbiYfVkJAQ5ebmSpKsVqscDkdJlwAAAAAAMLgSD6t169ZVUlKSJOnw4cOqVKlSSZcAAAAAADC4Eg+rbdq00ffff69PPvlEW7ZsUatWrUq6hGsWHR3t7RI8ht58j7/2JdGbr/LX3vy1L4nefBW9+R5/7UuiN1/lK72V+AWWJCkzM1O//fabGjdurHLlypX04QEAAAAABueVsAoAAAAAwJWU+DJgAAAAAACuhrAKAAAAADAcwioAAAAAwHAIq4U0c+ZMb5dQLI4cOSJJcjgc2r59u1asWKH4+HgvV1X8Vq1apXfeeUeffvqp0tLSvF1OkcyePVsHDx70dhkec+DAAZ09e1YOh0Nr167Vd999p5ycHG+XVSx2796tbdu2+U0/Fzt+/LjWr1+vZcuWaenSpdq0aZPOnTvn7bJwHbLb7dq+fbv27dvn9viWLVu8VFHxycjI0G+//Sar1arc3Fxt2bJFv/32m7fL8gg+Z/kWf/qcJfFZy4i4wNIVvPLKK8rNzXW7YvGhQ4dUu3ZtjR071nuFFYPx48dr7Nixevfdd1WqVClVrVpVP/30kxo2bKgHHnjA2+UVyS+//KJWrVpp27Zt2rdvn9q3b6/9+/drzZo1ev31171d3jUbMWKEoqKidPbsWXXp0kWNGzf2dknF5sMPP1R2drZSU1NVrVo1Va1aVfv27VNWVpZGjRrl7fKK5LPPPlNqaqrKlCmjnTt3qmPHjurRo4eCgoK8XVqRLV68WKdPn9aNN96osLAwWa1WHTp0SFu3btXYsWNVtmxZb5eI68jkyZNVrlw5nT17VllZWRo6dKgqVKjg+vvOV2VkZGj8+PFq3ry59u3bJ4vFogYNGigrK0tOp1OPPfaYt0u8ZnzO8k3++jlL4rOWEVm8XYCRjRw5Up999plCQkL04IMPKiwszOf/0vur1NRUPf/885KkLl266JVXXvGLX6Lr1q1TxYoV1adPH5UtW1bVqlXT2rVrvV1akYSHh2vw4ME6ffq0vv32W/3nP/9R/fr11ahRI918883eLq9Ijhw5ovHjxys7O1srV65Uz5495XA49Oijj3q7tCLbt2+fxo8fL0navn271q5dq9GjR6tnz55q3769l6srmh07dlzyweTmm2+W1WpVYmKibr31Vi9VVjRjx45VTk6OQkNDL/ucL/Pn3nJycvT4449Lkn7//XdNnjxZAwcO9HJVRXfw4EG1bdtWffv21e7du7VlyxbX39Pjxo3zbnFFxOcs3+Svn7MkPmsZEWH1CsqVK6fnnntO8fHxio2NVdeuXb1dUrE5ceKEvvjiC505c0bp6ekqV66ca8mKr3v88ceVmJioWbNm6dSpU4qOjtaJEyf8ZpYnIiJCAwYMkNPpVGJionbu3Onzv0BDQkK0adMmHTt2TMnJybJarTp8+LAiIyO9XVqRhYaGat++fapTp4727t2r3r17q3bt2vr666+9XVqRVatWTdOnT9dtt92miIgI5ebmKjExUfHx8br33nu9Xd41Gz58uKZPn64XXnhBYWFh3i6nWPlzb2azWbt27VLTpk3VoEEDjRo1SlOnTtXhw4e9XVqR1K1bVwsWLFDTpk3VuHFj10zPxo0bZbH49sc4Pmf5Jn//nCX592eto0eP6vjx4z7zWYtlwAVks9m0dOlS7dq1yzVL4sscDocOHz6svXv3qn79+qpZs6b+/e9/67777lP16tW9XV6xsNvt2rBhgw4dOqTKlSurc+fOCgkJ8XZZ12z16tXq0qWLt8vwiKysLG3cuFGlS5dWVFSUvvjiC+Xm5qpv376qV6+et8srkpMnT2revHk6fvy4brnlFt1zzz3eLqlY/fTTT0pISFBOTo5CQkJUt25dtWnT5rIzd74kKytLAQEBPv07Iz/+2lt2drZ++OEHt9+TNptN3333nc//7kxPT9eRI0fUtGlT12PLli1TdHS0wsPDvVhZ8bnwOSs+Pt7nZ4yl/D9n3XvvvapRo4a3yysW/vY5S7o+P2vdfffdql+/vrfLyxdh9Qrsdrt+/fVXlS1b1u1N3LJli/72t795sbKiozff4699Sf7f244dO1SuXDm/6y0jI0MHDx5UgwYNZDKZtH37dpUqVUrNmjXzdmlFcnFfZrNZ27dvV3h4uM/3dcHx48eVlJSkM2fOyOl0qmLFimrVqpXP/wODRG++6OK+HA6HKlWq5Bd9Sf77nkn/6y09PV1Op5P3zUf44s+bb68f8bCpU6de9mINq1ev9vkPmfTme/y1L4nefNHFF31ZvHixAgICXBd9+fnnn332oi8X97Vo0SK3i9n4cl8XXHxRrIiICNdFsRYtWuTzF8VavHix0tLS1KRJE3rzEf7+/Xg99Fa3bl2/7c0f3zdf/D1CWL0Cf71Yg0RvvshqtfplX5L/vmeS//bmrxd98de+LvDXi2JJ9OaL/LUvid58Fb0ZD/dZvYILF2uQ5LpYw9KlS33+Yg0SvfmigIAAv+xL8t/3TPLf3urWreu6p2Xjxo1dM46+ftEXf+3rggsXxfr111/1xx9/aN++fVq+fLni4+N9fpkzvfkef+1LojdfRW/GwzmrV+DPF2ugN9/jr31J9Oar/PWiL/7a1wX+elEsid58kb/2JdGbr6I3g3GiQBYuXOjtEjyG3nyPv/bldNKbr/LX3vy1L6eT3nyVv/bmr305nfTmq+jNGFgGXEDx8fHeLsFj6M33+GtfEr35Kn/tzV/7kujNV/lrb/7al0RvvorejIGwCgAAAAAwHMIqAAAAAMBwCKsFVKVKFW+X4DH05nv8tS+J3nyVv/bmr31J9Oar/LU3f+1LojdfRW/GwNWAr2D27NmKjo5WnTp1vF1KsaM33+OvfUn05qv8tTd/7euCAwcOKDIyUuHh4Vq/fr1MJpPatm2r4OBgb5dWZPTme/y1L4nefBW9GYvv3zTOg/bu3auAgAAtW7ZMXbp0UePGjb1dUrGhN9/jr31J9Oar/LU3f+1Lkj788ENlZ2crNTVV1apVU9WqVbVv3z5t2bJFo0aN8nZ5RUJvvsdf+5LozVfRm/EQVq8gPDxcgwcP1unTp/Xtt9/qiy++0A033KBGjRrp5ptv9nZ5RUJvvsdf+5LozVf5a2/+2pckHTlyROPHj1d2dra++eYb9ezZUw6HQ48++qi3SysyevM9/tqXRG++it6Mh7BaABERERowYICcTqcSExO1c+dOn//AcgG9+R5/7UuiN1/lr735Y18hISHatGmTjh07puTkZFmtVv3xxx+KjIz0dmlFRm++x1/7kujNV9Gb8XDO6hWsXr1azZo1U1JSks6cOSOn06mKFSuqVatWCg0N9XZ5RUJvvsdf+5LozVf5a2/+2pckZWVlacOGDbJarXI4HPrqq68kSePGjVOjRo28XF3R0Jvv8de+JHrzVfRmPITVK1i8eLFOnz6tG2+8UWFhYbJarTp06JC2bt2qsWPHqmzZst4u8ZrRm+/x174kevNV/tqbv/Yl0Zuv8tfe/LUvid58Fb0ZD8uAr2DHjh16/fXX3R67+eabZbValZiYqFtvvdVLlRUdvfkef+1Lojdf5a+9+WtfEr35Kn/tzV/7kujNV9Gb8RBWr6BatWqaPn26brvtNkVERCg3N1eJiYmKj4/Xvffe6+3yioTefI+/9iXRm6/y1978tS+J3nyVv/bmr31J9Oar6M14WAZ8FT/99JMSEhKUk5OjkJAQ1a1bV23atPH585YkevNF/tqXRG++yl9789e+JHrzVf7am7/2JdGbr6I3YyGsAgAAAAAMx+ztAgAAAAAA+CvCKgAAF8nJyVFBFx05HA4PV1PyWHAFADAKwioA4Lp27NgxjRw50vX1iy++qKysLLdtbDabJGn9+vWaM2eOJGnbtm0aN27cNYW7Z599VsnJyW6P/ec//9GxY8ckSbm5uV4JwnPnztWyZctK/LgAAFwOVwMGAFyXRo0aJZPJJLvdrmPHjmn06NGSpDNnzmjSpElyOp2yWCwaP368Fi1apNOnT6tJkyayWCyy2Wz6/PPPlZOT4wq6ERERGjFihM6ePet2e4AGDRpoyJAhbscOCAhQQECA22NVqlTR1KlTFRMTo5EjR7ptY7PZZLPZNG3atCv29Morr8hsNstiufxf7zabTU6nU5MmTVJWVpY++ugjt+ePHTumM2fO6I8//nA9ZrFYNHTo0CseFwAATyCsAgCuS2+88YYk6eTJk5ozZ45effVVSdJrr72mkSNHKjw83LVt3759NXPmTJ0+fVqS9N5776lt27a67777JJ2fkYyMjJR0fmlwdna23n//fSUkJOjrr7/W5s2blZmZKbPZLJPJJKvVqs2bN6t+/fq68cYbtWDBAvXq1UvVqlVTcHCwpkyZ4lbryZMnFRsbe9WeAgIC9MILL6h8+fKyWCwymUySzi/tzcvLU3p6ut59913XY4mJiYqJibnimC+++CJhFQDgFYRVAMB1adu2bUpKSlLVqlXVtGlT1+OVK1dWRkaG1qxZo7vvvluSFBQUpP79+2v69OkqW7asypQpo2rVqmnOnDm6/fbbdfDgQT300EOSJLPZ/Qwbs9ksp9PpWi588bLhC3/+7rvv1L17d2VmZrpC8wWPPPKIIiIiCtTThZnYd999V2lpaUpNTVVwcLBKly6tvLw8vfjii64A63A4VK5cOVWoUEEvvfSSQkNDlZeX55rRzc3N1ZNPPqlWrVoV9CUFAKBYEVYBANelFi1a6KuvvtL27dv11ltvuR7ftWuXnnrqKe3bt09JSUmqXbu2VqxYoR9//FFRUVEKDAzUww8/LLvdrvXr12vGjBkaOXKka+mtyWTS6dOn9fLLL8tqtapGjRpq27atjh8/rsqVK0uSVq5cqfbt26tChQqSzofMCzOuUVFReuSRRyRJ77//vnJzcwvd27Bhw2Q2m7Vw4UJVq1ZNf/vb32S325WWlubapkyZMpo4caIkaeLEiVq6dKl+/vlnvfbaaypdurQk6eeff9awYcMK/+ICAFAMCKsAgOtORkaGJk2apO7du+ubb77Rvn37NGfOHFksFp09e1avvfaaHA6H5s+fr9GjR6ts2bJ64403tGXLFu3fv18bNmxQQkKCmjdvrjvvvFNBQUGusU0mkyIiIhQbG6uEhAT997//VXZ2tsaPH69Ro0apevXq+dZlMpm0adMmJSUlSZJOnTql9u3bF7q/BQsWaPfu3a6Z1ZUrV6pKlSquZcvS+fNXk5KS9Msvv2jnzp1q3769srKydPjwYTVo0EBz585VcnKyGjZsqDJlyhS6BgAAioqwCgC47pQuXVqjR49WfHy80tLSdPz4cU2cOFHp6el65plnNGTIENWrV095eXmSpDvuuENms1kdOnRQhw4dFBcXp+rVq6tHjx5u4zocjsteHTgsLEx9+/bV2rVrNWjQoCvW1rZtW7eZ1WsxcOBASedDa7Vq1ZSQkKB77rnH7QrDubm5WrZsmdq0aaO77rpL8+fPV6NGjTRjxgyVKlVKVqtV06ZNcy0bBgCgpBFWAQDXnby8PNeM6oABA5Senq7AwEBt3LhRt9xyi1asWKFhw4YpMDBQ27Zt07x58xQUFOQKbsePH5fT6dTmzZtdY9psNvXr10+NGzd2WwZcrVo1SdKdd9551eBXXLersdlsblcEbtGihVJTU1WuXDnXY2FhYRowYIA2bNigdevWacCAATp48KC2bdumhx56SOvWrdPevXvVoEGDYqkJAIDCIqwCAK47OTk5CgkJ0T/+8Q99/fXXKl26tI4fP65NmzbpzTff1NSpUxUXF6fo6Gi1bt1arVu3du27f/9+vfvuu4qIiFDHjh3VoUMHt7HT0tLclgFfuG9pQWYog4KCFB8f77rIUl5enjp16lSo3lJTUzV16lRXWN21a5ek/4XpC7KysjR//nzdcsstateunRYsWKCmTZuqb9++SktLU79+/fTee+9pxIgRV1y6DACApxBWAQDXnVKlSqlDhw768ccftWHDBg0aNEhvvfWWHn/8cYWEhOiZZ57R2LFjdfz4cQ0cOFBms1kpKSmKi4vT9u3b9fLLLysyMlIzZszQxo0bdeedd6pZs2YKCwtTYGCgmjVrJklq1KiRGjZs6DpuZmamUlNTlZGR4Xaf1Qv3P/1rML7g0KFDbtsPGTJE06dPV2BgoNt2NptNFSpUcF046a9Onjwpu90u6Xww7tSpk9atW6effvpJgwcPVsWKFTV9+nQ1btxYDRs21N13362RI0fqn//8pxo1anTtLzgAANeAsAoAuC4dOnRIiYmJevXVV5WUlKQHH3xQTZo0kXT+Srnjx49XQkKCzp07p1dffVUBAQGKjo5WTEyMgoODJUnDhw/Xrl27tGbNGs2ePVuvvPKKGjRooCeffFLS+dvWXHwrG5PJpJkzZ6pDhw5uS3KtVqtsNttl69y6davee+899e7d2/VYhw4dLgmq0vllxBfPqv6VzWZzLTXOysrS5s2bdffdd6tRo0ZKT0/XI488optuusl1UafOnTsrKipKVatWLejLCgBAsTE5L3clCAAA4JKRkeG6nUt+nE6nRy5GlJubK4fDoZCQENdjSUlJioqKuuy2gYGB11xHbm6u25WNAQDwJsIqAAAAAMBwzFffBAAAAACAkkVYBQAAAAAYDmEVAAAAAGA4hFUAAAAAgOEQVgEAAAAAhvP/z7xgNLTFsdcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = pd.pivot_table(df2[df2.国家 == '中国'], index=['获奖时间','国家'], columns=df2.奖牌类型.map({1:'金牌',2:'银牌',3:'铜牌'}), values='奖牌类型',aggfunc=np.size)\n",
    "temp.fillna(0, inplace=True)\n",
    "\n",
    "ax = temp.plot.bar(figsize=(16,8), y=['金牌','银牌','铜牌'], ylabel='奖牌数', stacked =True, grid=True, width=0.8, alpha=0.9)\n",
    "ax.set_xticklabels([col[0] for col in temp.index])\n",
    "plt.title('中国队获奖时间分布表', fontsize=16)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = pd.pivot_table( data = df2[df2.国家id.isin(df1.loc[df1.排名.sort_values()[:3].index]['国家id'].values)], \n",
    "    index='获奖时间', columns='国家', values='奖牌类型',aggfunc=np.size).fillna(0)\n",
    "\n",
    "ax = temp.plot.bar(figsize=(16,8), width=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th colspan=\"3\" halign=\"left\">中国</th>\n",
       "      <th colspan=\"3\" halign=\"left\">日本</th>\n",
       "      <th colspan=\"3\" halign=\"left\">美国</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>奖牌类型</th>\n",
       "      <th>金牌</th>\n",
       "      <th>铜牌</th>\n",
       "      <th>银牌</th>\n",
       "      <th>金牌</th>\n",
       "      <th>铜牌</th>\n",
       "      <th>银牌</th>\n",
       "      <th>金牌</th>\n",
       "      <th>铜牌</th>\n",
       "      <th>银牌</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>获奖时间</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>07-24</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-25</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-27</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-28</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-29</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-30</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-31</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-01</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-02</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-03</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-05</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-06</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-07</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "国家      中国             日本             美国          \n",
       "奖牌类型    金牌   铜牌   银牌   金牌   铜牌   银牌   金牌   铜牌   银牌\n",
       "获奖时间                                              \n",
       "07-24  3.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0\n",
       "07-25  3.0  3.0  1.0  4.0  0.0  0.0  4.0  3.0  2.0\n",
       "07-26  0.0  3.0  4.0  4.0  3.0  1.0  3.0  1.0  1.0\n",
       "07-27  3.0  0.0  0.0  2.0  2.0  1.0  2.0  4.0  5.0\n",
       "07-28  3.0  2.0  1.0  3.0  0.0  1.0  2.0  1.0  3.0\n",
       "07-29  3.0  0.0  1.0  2.0  1.0  0.0  3.0  1.0  3.0\n",
       "07-30  4.0  2.0  3.0  2.0  1.0  0.0  0.0  1.0  2.0\n",
       "07-31  2.0  1.0  3.0  0.0  1.0  1.0  2.0  2.0  1.0\n",
       "08-01  3.0  1.0  1.0  0.0  1.0  0.0  4.0  3.0  6.0\n",
       "08-02  5.0  3.0  3.0  0.0  1.0  1.0  2.0  1.0  2.0\n",
       "08-03  3.0  0.0  4.0  2.0  1.0  0.0  2.0  4.0  3.0\n",
       "08-04  0.0  0.0  1.0  2.0  1.0  1.0  1.0  2.0  3.0\n",
       "08-05  2.0  0.0  2.0  1.0  2.0  3.0  4.0  4.0  4.0\n",
       "08-06  2.0  1.0  2.0  2.0  2.0  1.0  2.0  4.0  1.0\n",
       "08-07  2.0  1.0  5.0  3.0  1.0  1.0  5.0  2.0  3.0\n",
       "08-08  0.0  0.0  1.0  0.0  0.0  2.0  3.0  0.0  2.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table( data = df2[df2.国家id.isin(df1.loc[df1.排名.sort_values()[:3].index]['国家id'].values)], \n",
    "    index='获奖时间', columns=['国家', df2.奖牌类型.map({1:'金牌',2:'银牌',3:'铜牌'})], values='运动员',aggfunc=np.size).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = pd.pivot_table( data = df2[df2.国家id.isin(df1.loc[df1.排名.sort_values()[:3].index]['国家id'].values)], \n",
    "    index='获奖时间', columns=['国家', df2.奖牌类型.map({1:'金牌',2:'银牌',3:'铜牌'})], values='运动员',aggfunc=np.size).fillna(0)\n",
    "\n",
    "X = pd.Series([i for i in range(len(temp))])\n",
    "BarWidth = 0.25\n",
    "Cols = temp.columns.levels[0].values\n",
    "\n",
    "plt.figure(figsize=(18,8))\n",
    "plt.title('前三名每日奖牌数')\n",
    "off = -BarWidth\n",
    "\n",
    "for c in Cols:\n",
    "    plt.bar(X+off, temp.loc[:,(c,'金牌')], width=BarWidth, alpha = 0.9)\n",
    "    plt.bar(X+off, temp.loc[:,(c,'银牌')], bottom=temp.loc[:,(c,'金牌')], width=BarWidth, alpha = 0.9)\n",
    "    plt.bar(X+off, temp.loc[:,(c,'铜牌')], bottom=temp.loc[:,(c,'金牌')] + temp.loc[:,(c,'银牌')],  width=BarWidth, alpha = 0.9)\n",
    "    off += BarWidth\n",
    "\n",
    "\n",
    "plt.legend(Cols)\n",
    "plt.xticks(X, temp.index)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>国家</th>\n",
       "      <th colspan=\"3\" halign=\"left\">中国</th>\n",
       "      <th colspan=\"3\" halign=\"left\">日本</th>\n",
       "      <th colspan=\"3\" halign=\"left\">美国</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>奖牌类型</th>\n",
       "      <th>金牌</th>\n",
       "      <th>铜牌</th>\n",
       "      <th>银牌</th>\n",
       "      <th>金牌</th>\n",
       "      <th>铜牌</th>\n",
       "      <th>银牌</th>\n",
       "      <th>金牌</th>\n",
       "      <th>铜牌</th>\n",
       "      <th>银牌</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>获奖时间</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>07-24</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-25</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-27</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-28</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-29</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-30</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-31</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-01</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-02</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-03</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-05</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-06</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-07</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "国家      中国             日本             美国          \n",
       "奖牌类型    金牌   铜牌   银牌   金牌   铜牌   银牌   金牌   铜牌   银牌\n",
       "获奖时间                                              \n",
       "07-24  3.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0\n",
       "07-25  3.0  3.0  1.0  4.0  0.0  0.0  4.0  3.0  2.0\n",
       "07-26  0.0  3.0  4.0  4.0  3.0  1.0  3.0  1.0  1.0\n",
       "07-27  3.0  0.0  0.0  2.0  2.0  1.0  2.0  4.0  5.0\n",
       "07-28  3.0  2.0  1.0  3.0  0.0  1.0  2.0  1.0  3.0\n",
       "07-29  3.0  0.0  1.0  2.0  1.0  0.0  3.0  1.0  3.0\n",
       "07-30  4.0  2.0  3.0  2.0  1.0  0.0  0.0  1.0  2.0\n",
       "07-31  2.0  1.0  3.0  0.0  1.0  1.0  2.0  2.0  1.0\n",
       "08-01  3.0  1.0  1.0  0.0  1.0  0.0  4.0  3.0  6.0\n",
       "08-02  5.0  3.0  3.0  0.0  1.0  1.0  2.0  1.0  2.0\n",
       "08-03  3.0  0.0  4.0  2.0  1.0  0.0  2.0  4.0  3.0\n",
       "08-04  0.0  0.0  1.0  2.0  1.0  1.0  1.0  2.0  3.0\n",
       "08-05  2.0  0.0  2.0  1.0  2.0  3.0  4.0  4.0  4.0\n",
       "08-06  2.0  1.0  2.0  2.0  2.0  1.0  2.0  4.0  1.0\n",
       "08-07  2.0  1.0  5.0  3.0  1.0  1.0  5.0  2.0  3.0\n",
       "08-08  0.0  0.0  1.0  0.0  0.0  2.0  3.0  0.0  2.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.loc[:,('中国','金牌')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 17 - 数据计算\n",
    "\n",
    "计算前十名各国每日奖牌数量合计\n",
    "\n",
    "注意：对于第一天没有数据的国家用0填充，其余时间的缺失值用上一日数据填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>国家</th>\n",
       "      <th>ROC</th>\n",
       "      <th>中国</th>\n",
       "      <th>德国</th>\n",
       "      <th>意大利</th>\n",
       "      <th>日本</th>\n",
       "      <th>法国</th>\n",
       "      <th>澳大利亚</th>\n",
       "      <th>美国</th>\n",
       "      <th>英国</th>\n",
       "      <th>荷兰</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>获奖时间</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>07-24</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-25</th>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-26</th>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-27</th>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-28</th>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-29</th>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-30</th>\n",
       "      <td>34.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-31</th>\n",
       "      <td>37.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-01</th>\n",
       "      <td>44.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-02</th>\n",
       "      <td>50.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-03</th>\n",
       "      <td>52.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-04</th>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-05</th>\n",
       "      <td>58.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-06</th>\n",
       "      <td>62.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-07</th>\n",
       "      <td>69.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-08</th>\n",
       "      <td>71.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "国家      ROC    中国    德国   意大利    日本    法国  澳大利亚     美国    英国    荷兰\n",
       "获奖时间                                                              \n",
       "07-24   2.0   4.0   0.0   2.0   1.0   0.0   0.0    0.0   0.0   1.0\n",
       "07-25   7.0  11.0   2.0   5.0   5.0   3.0   3.0    9.0   2.0   2.0\n",
       "07-26  12.0  18.0   3.0   9.0  13.0   5.0   6.0   14.0   7.0   3.0\n",
       "07-27  18.0  21.0   5.0  12.0  18.0   7.0   9.0   25.0  13.0   3.0\n",
       "07-28  23.0  27.0  10.0  15.0  22.0   8.0  16.0   31.0  16.0  11.0\n",
       "07-29  28.0  31.0  13.0  19.0  25.0  11.0  20.0   38.0  18.0  13.0\n",
       "07-30  34.0  40.0  16.0  20.0  28.0  13.0  22.0   41.0  24.0  15.0\n",
       "07-31  37.0  46.0  17.0  24.0  30.0  19.0  27.0   46.0  28.0  16.0\n",
       "08-01  44.0  51.0  19.0  27.0  31.0  21.0  31.0   59.0  32.0  17.0\n",
       "08-02  50.0  62.0  23.0  28.0  33.0  23.0  33.0   64.0  35.0  18.0\n",
       "08-03  52.0  69.0  30.0  29.0  36.0  24.0  33.0   73.0  43.0  20.0\n",
       "08-04  53.0  70.0  32.0  30.0  40.0  25.0  36.0   79.0  48.0  23.0\n",
       "08-05  58.0  74.0  34.0  35.0  46.0  27.0  41.0   91.0  51.0  26.0\n",
       "08-06  62.0  79.0  36.0  38.0  51.0  27.0  44.0   98.0  58.0  31.0\n",
       "08-07  69.0  87.0  37.0  39.0  56.0  32.0  46.0  108.0  63.0  33.0\n",
       "08-08  71.0  88.0  37.0  40.0  58.0  33.0  46.0  113.0  65.0  36.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table( data = df2[df2.国家id.isin(df1.loc[df1.排名.sort_values()[:10].index]['国家id'].values)], \n",
    "    index='获奖时间', columns='国家', values='奖牌类型',aggfunc=np.size).fillna(0).cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='获奖时间'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.pivot_table( data = df2[df2.国家id.isin(df1.loc[df1.排名.sort_values()[:10].index]['国家id'].values)], \n",
    "    index='获奖时间', columns='国家', values='奖牌类型',aggfunc=np.size).fillna(0).cumsum().plot(kind='line', figsize=(16,8))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据可视化\n",
    "\n",
    "下面是一些数据可视化的操作\n",
    "\n",
    "由于部分是之前未涉及的内容，所以以下全部习题我将保留答案。也不可使用 ans 查看答案"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 18 - 条形图\n",
    "\n",
    "对金牌数量排行前10的国家制作条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 32654 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 22269 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 20013 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 26412 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 33521 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 28595 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 22823 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 21033 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 20122 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 33655 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 20848 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 27861 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 24503 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 24847 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Songti SC\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 19996 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 20140 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 22885 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 36816 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 20250 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 37329 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 29260 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 25490 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 34892 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 27036 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 32654 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 22269 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 20013 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 26085 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 26412 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 33521 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 28595 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 22823 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 21033 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 20122 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 33655 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 20848 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 27861 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 24503 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 24847 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 19996 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 20140 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 22885 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 36816 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 20250 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 37329 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 29260 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 25490 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 34892 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\Users\\songzhao\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 27036 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 528,
       "width": 865
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "plt.rcParams['font.sans-serif'] = ['Songti SC']\n",
    "\n",
    "n = 10\n",
    "x = [i for i in range(n)]  # 共有多少个数据就设置多少个i\n",
    "y1 = [40] * n  # 这里是x轴最大范围，也就是浅色部分长度，需要根据你的数据进行修改\n",
    "counts = list(df1.head(10)['金牌数'])\n",
    "countrys = list(df1.head(10)['国家奥委会'])\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 6), dpi=100)\n",
    "\n",
    "plt.xlim(0, 40.5)  # 这里的1310修改成最大的x轴，比上面的y1大一点即可\n",
    "\n",
    "plt.barh(x, y1, color='#EDF6FF', alpha=0.99)\n",
    "\n",
    "for i in range(n):\n",
    "    plt.barh(x[i], counts[::-1][i], color='#00C5D2' if x[i] %\n",
    "             2 == 0 else '#5172F0', height=0.4, ec='#333', lw=0.8)\n",
    "\n",
    "ax = plt.gca()\n",
    "\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.tick_params(direction='in')\n",
    "\n",
    "ax.tick_params(bottom=False, top=True, left=False, right=False)\n",
    "\n",
    "plt.yticks([])\n",
    "\n",
    "plt.ylim(-0.7, 9.7)\n",
    "for i in range(n):\n",
    "\n",
    "    plt.text(-3.7, 8.9-i, f'{countrys[i]}', bbox=dict(boxstyle=\"round\", fc=\"#EDF6FF\",\n",
    "                                                      ec=\"black\", alpha=0.9, linestyle='--', linewidth=0.8), size=10)\n",
    "\n",
    "    plt.text(counts[::-1][i]+0.25, i, f\"{counts[::-1][i]}\", fontsize=8.5, color='#00C5D2' if x[i] %\n",
    "             2 == 0 else '#5172F0', verticalalignment='center',\n",
    "             style='italic', family='serif', weight='heavy')\n",
    "\n",
    "\n",
    "plt.text(0, 10.5, '东京奥运会金牌排行榜', size=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 19 - 堆叠图\n",
    "\n",
    "将排行榜前十名的奖牌绘制堆叠图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
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       "    });\n",
       "</script>\n",
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       "        <div id=\"c18fc76cfd9147a1910f848e194de573\" style=\"width:900px; height:500px;\"></div>\n",
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       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
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       "            \"barGap\": \"30%\",\n",
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       "        {\n",
       "            \"type\": \"bar\",\n",
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       "            \"legendHoverLink\": true,\n",
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       "            \"barCategoryGap\": \"20%\",\n",
       "            \"barGap\": \"30%\",\n",
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       "            \"largeThreshold\": 400,\n",
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       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u91d1\\u724c\\ud83c\\udfc5\\ufe0f\",\n",
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       "                \"\\u94dc\\u724c\\ud83e\\udd49\": true\n",
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       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"\\u7f8e\\u56fd\",\n",
       "                \"\\u4e2d\\u56fd\",\n",
       "                \"\\u65e5\\u672c\",\n",
       "                \"\\u82f1\\u56fd\",\n",
       "                \"ROC\",\n",
       "                \"\\u6fb3\\u5927\\u5229\\u4e9a\",\n",
       "                \"\\u8377\\u5170\",\n",
       "                \"\\u6cd5\\u56fd\",\n",
       "                \"\\u5fb7\\u56fd\",\n",
       "                \"\\u610f\\u5927\\u5229\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u4e1c\\u4eac\\u5965\\u8fd0\\u4f1a\\u699c10\\u5956\\u724c\\u5206\\u5e03\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_c18fc76cfd9147a1910f848e194de573.setOption(option_c18fc76cfd9147a1910f848e194de573);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x7fd2f0ee5198>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Bar\n",
    "from pyecharts.globals import ThemeType\n",
    "gold = list(df1['金牌数'].head(10))\n",
    "silver = list(df1['银牌数'].head(10))\n",
    "bronze = list(df1['铜牌数'].head(10))\n",
    "countrys = list(df1['国家奥委会'].head(10))\n",
    "\n",
    "\n",
    "\n",
    "c = (\n",
    "    Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK))\n",
    "    .add_xaxis(countrys)\n",
    "    .add_yaxis(\"金牌🏅️\", gold, stack=\"stack1\",color =\"#8F3605\" )\n",
    "    .add_yaxis(\"银牌🥈\", silver, stack=\"stack1\", color=\"#838383\")\n",
    "    .add_yaxis(\"铜牌🥉\", bronze, stack=\"stack1\", color='#FBED67')\n",
    "    \n",
    "    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"东京奥运会榜10奖牌分布\"))\n",
    ")\n",
    "\n",
    "c.render_notebook()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"91f8058aaf514c0e9fc935459b703615\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_91f8058aaf514c0e9fc935459b703615 = echarts.init(\n",
       "                    document.getElementById('91f8058aaf514c0e9fc935459b703615'), 'dark', {renderer: 'canvas'});\n",
       "                var option_91f8058aaf514c0e9fc935459b703615 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"\\u91d1\\u724c\\ud83c\\udfc5\\ufe0f\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"data\": [\n",
       "                39,\n",
       "                38,\n",
       "                27,\n",
       "                22,\n",
       "                20,\n",
       "                17,\n",
       "                10,\n",
       "                10,\n",
       "                10,\n",
       "                10\n",
       "            ],\n",
       "            \"showBackground\": false,\n",
       "            \"stack\": \"stack1\",\n",
       "            \"barMinHeight\": 0,\n",
       "            \"barCategoryGap\": \"20%\",\n",
       "            \"barGap\": \"30%\",\n",
       "            \"large\": false,\n",
       "            \"largeThreshold\": 400,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"clip\": true,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        },\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"\\u94f6\\u724c\\ud83e\\udd48\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"data\": [\n",
       "                41,\n",
       "                32,\n",
       "                14,\n",
       "                21,\n",
       "                28,\n",
       "                7,\n",
       "                12,\n",
       "                12,\n",
       "                11,\n",
       "                10\n",
       "            ],\n",
       "            \"showBackground\": false,\n",
       "            \"stack\": \"stack1\",\n",
       "            \"barMinHeight\": 0,\n",
       "            \"barCategoryGap\": \"20%\",\n",
       "            \"barGap\": \"30%\",\n",
       "            \"large\": false,\n",
       "            \"largeThreshold\": 400,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"clip\": true,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        },\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"\\u94dc\\u724c\\ud83e\\udd49\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"data\": [\n",
       "                33,\n",
       "                18,\n",
       "                17,\n",
       "                22,\n",
       "                23,\n",
       "                22,\n",
       "                14,\n",
       "                11,\n",
       "                16,\n",
       "                20\n",
       "            ],\n",
       "            \"showBackground\": false,\n",
       "            \"stack\": \"stack1\",\n",
       "            \"barMinHeight\": 0,\n",
       "            \"barCategoryGap\": \"20%\",\n",
       "            \"barGap\": \"30%\",\n",
       "            \"large\": false,\n",
       "            \"largeThreshold\": 400,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"clip\": true,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u91d1\\u724c\\ud83c\\udfc5\\ufe0f\",\n",
       "                \"\\u94f6\\u724c\\ud83e\\udd48\",\n",
       "                \"\\u94dc\\u724c\\ud83e\\udd49\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u91d1\\u724c\\ud83c\\udfc5\\ufe0f\": true,\n",
       "                \"\\u94f6\\u724c\\ud83e\\udd48\": true,\n",
       "                \"\\u94dc\\u724c\\ud83e\\udd49\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"\\u7f8e\\u56fd\",\n",
       "                \"\\u4e2d\\u56fd\",\n",
       "                \"\\u65e5\\u672c\",\n",
       "                \"\\u82f1\\u56fd\",\n",
       "                \"ROC\",\n",
       "                \"\\u6fb3\\u5927\\u5229\\u4e9a\",\n",
       "                \"\\u8377\\u5170\",\n",
       "                \"\\u6cd5\\u56fd\",\n",
       "                \"\\u5fb7\\u56fd\",\n",
       "                \"\\u610f\\u5927\\u5229\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u4e1c\\u4eac\\u5965\\u8fd0\\u4f1a\\u699c10\\u5956\\u724c\\u5206\\u5e03\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_91f8058aaf514c0e9fc935459b703615.setOption(option_91f8058aaf514c0e9fc935459b703615);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x7fd2f0edacf8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Bar\n",
    "from pyecharts.globals import ThemeType\n",
    "gold = list(df1['金牌数'].head(10))\n",
    "silver = list(df1['银牌数'].head(10))\n",
    "bronze = list(df1['铜牌数'].head(10))\n",
    "countrys = list(df1['国家奥委会'].head(10))\n",
    "\n",
    "c = (\n",
    "    Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK))\n",
    "    .add_xaxis(countrys)\n",
    "    .add_yaxis(\"金牌🏅️\", gold, stack=\"stack1\",color =\"#8F3605\" )\n",
    "    .add_yaxis(\"银牌🥈\", silver, stack=\"stack1\", color=\"#838383\")\n",
    "    .add_yaxis(\"铜牌🥉\", bronze, stack=\"stack1\", color='#FBED67')\n",
    "    \n",
    "    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"东京奥运会榜10奖牌分布\"))\n",
    ")\n",
    "\n",
    "c.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 20 - 饼图\n",
    "\n",
    "绘制中国队的奖牌分布饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.pivot_table(df2,values = ['奖牌类型'],index = ['国家','运动类别'],aggfunc = 'count').query(\"国家 == ['中国']\")\n",
    "jiangpai = list(data['奖牌类型'])\n",
    "xiangmu = [data.index[i][1] for i in range(len(data))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"72f5b7f1c7114f6eb531fce4f5adade8\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_72f5b7f1c7114f6eb531fce4f5adade8 = echarts.init(\n",
       "                    document.getElementById('72f5b7f1c7114f6eb531fce4f5adade8'), 'dark', {renderer: 'canvas'});\n",
       "                var option_72f5b7f1c7114f6eb531fce4f5adade8 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"pie\",\n",
       "            \"clockwise\": true,\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u4e09\\u4eba\\u7bee\\u7403\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e3e\\u91cd\",\n",
       "                    \"value\": 8\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e52\\u4e53\\u7403\",\n",
       "                    \"value\": 7\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u51fb\\u5251\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u53e4\\u5178\\u5f0f\\u6454\\u8de4\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u573a\\u5730\\u81ea\\u884c\\u8f66\\u8d5b\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5c04\\u51fb\",\n",
       "                    \"value\": 11\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5e06\\u8239\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u62f3\\u51fb\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6e38\\u6cf3\",\n",
       "                    \"value\": 6\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7530\\u5f84\",\n",
       "                    \"value\": 5\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7a7a\\u624b\\u9053\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7ade\\u6280\\u4f53\\u64cd\",\n",
       "                    \"value\": 8\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7fbd\\u6bdb\\u7403\",\n",
       "                    \"value\": 6\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u81ea\\u7531\\u5f0f\\u6454\\u8de4\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u82b1\\u6837\\u6e38\\u6cf3\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8d5b\\u8247\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8dc6\\u62f3\\u9053\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8df3\\u6c34\",\n",
       "                    \"value\": 12\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8e66\\u5e8a\\u4f53\\u64cd\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9759\\u6c34\\u76ae\\u5212\\u8247\",\n",
       "                    \"value\": 3\n",
       "                }\n",
       "            ],\n",
       "            \"radius\": [\n",
       "                \"40%\",\n",
       "                \"75%\"\n",
       "            ],\n",
       "            \"center\": [\n",
       "                \"50%\",\n",
       "                \"50%\"\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8,\n",
       "                \"formatter\": \"{b}: {c}\"\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u4e09\\u4eba\\u7bee\\u7403\",\n",
       "                \"\\u4e3e\\u91cd\",\n",
       "                \"\\u4e52\\u4e53\\u7403\",\n",
       "                \"\\u51fb\\u5251\",\n",
       "                \"\\u53e4\\u5178\\u5f0f\\u6454\\u8de4\",\n",
       "                \"\\u573a\\u5730\\u81ea\\u884c\\u8f66\\u8d5b\",\n",
       "                \"\\u5c04\\u51fb\",\n",
       "                \"\\u5e06\\u8239\",\n",
       "                \"\\u62f3\\u51fb\",\n",
       "                \"\\u6e38\\u6cf3\",\n",
       "                \"\\u7530\\u5f84\",\n",
       "                \"\\u7a7a\\u624b\\u9053\",\n",
       "                \"\\u7ade\\u6280\\u4f53\\u64cd\",\n",
       "                \"\\u7fbd\\u6bdb\\u7403\",\n",
       "                \"\\u81ea\\u7531\\u5f0f\\u6454\\u8de4\",\n",
       "                \"\\u82b1\\u6837\\u6e38\\u6cf3\",\n",
       "                \"\\u8d5b\\u8247\",\n",
       "                \"\\u8dc6\\u62f3\\u9053\",\n",
       "                \"\\u8df3\\u6c34\",\n",
       "                \"\\u8e66\\u5e8a\\u4f53\\u64cd\",\n",
       "                \"\\u9759\\u6c34\\u76ae\\u5212\\u8247\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"left\": \"2%\",\n",
       "            \"top\": \"15%\",\n",
       "            \"orient\": \"vertical\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u4e2d\\u56fd\\u961f\\u5956\\u724c\\u5206\\u5e03\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_72f5b7f1c7114f6eb531fce4f5adade8.setOption(option_72f5b7f1c7114f6eb531fce4f5adade8);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x7fd2ef9772b0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Pie\n",
    "\n",
    "c = (\n",
    "    Pie(init_opts=opts.InitOpts(theme=ThemeType.DARK))\n",
    "    .add(\n",
    "        \"\",\n",
    "        [list(z) for z in zip(xiangmu, jiangpai)],\n",
    "        radius=[\"40%\", \"75%\"],\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"中国队奖牌分布\"),\n",
    "        legend_opts=opts.LegendOpts(orient=\"vertical\", pos_top=\"15%\", pos_left=\"2%\"),\n",
    "    )\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\n",
    "    .render_notebook()\n",
    ")\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"2d4e35a86559481cbf6018915ed78d7d\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_2d4e35a86559481cbf6018915ed78d7d = echarts.init(\n",
       "                    document.getElementById('2d4e35a86559481cbf6018915ed78d7d'), 'light', {renderer: 'canvas'});\n",
       "                var option_2d4e35a86559481cbf6018915ed78d7d = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"pie\",\n",
       "            \"clockwise\": true,\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u4e09\\u4eba\\u7bee\\u7403\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e3e\\u91cd\",\n",
       "                    \"value\": 8\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e52\\u4e53\\u7403\",\n",
       "                    \"value\": 7\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u51fb\\u5251\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u53e4\\u5178\\u5f0f\\u6454\\u8de4\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u573a\\u5730\\u81ea\\u884c\\u8f66\\u8d5b\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5c04\\u51fb\",\n",
       "                    \"value\": 11\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5e06\\u8239\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u62f3\\u51fb\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6e38\\u6cf3\",\n",
       "                    \"value\": 6\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7530\\u5f84\",\n",
       "                    \"value\": 5\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7a7a\\u624b\\u9053\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7ade\\u6280\\u4f53\\u64cd\",\n",
       "                    \"value\": 8\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7fbd\\u6bdb\\u7403\",\n",
       "                    \"value\": 6\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u81ea\\u7531\\u5f0f\\u6454\\u8de4\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u82b1\\u6837\\u6e38\\u6cf3\",\n",
       "                    \"value\": 2\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8d5b\\u8247\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8dc6\\u62f3\\u9053\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8df3\\u6c34\",\n",
       "                    \"value\": 12\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8e66\\u5e8a\\u4f53\\u64cd\",\n",
       "                    \"value\": 3\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9759\\u6c34\\u76ae\\u5212\\u8247\",\n",
       "                    \"value\": 3\n",
       "                }\n",
       "            ],\n",
       "            \"radius\": [\n",
       "                \"40%\",\n",
       "                \"75%\"\n",
       "            ],\n",
       "            \"center\": [\n",
       "                \"50%\",\n",
       "                \"50%\"\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8,\n",
       "                \"formatter\": \"{b}: {c}\"\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u4e09\\u4eba\\u7bee\\u7403\",\n",
       "                \"\\u4e3e\\u91cd\",\n",
       "                \"\\u4e52\\u4e53\\u7403\",\n",
       "                \"\\u51fb\\u5251\",\n",
       "                \"\\u53e4\\u5178\\u5f0f\\u6454\\u8de4\",\n",
       "                \"\\u573a\\u5730\\u81ea\\u884c\\u8f66\\u8d5b\",\n",
       "                \"\\u5c04\\u51fb\",\n",
       "                \"\\u5e06\\u8239\",\n",
       "                \"\\u62f3\\u51fb\",\n",
       "                \"\\u6e38\\u6cf3\",\n",
       "                \"\\u7530\\u5f84\",\n",
       "                \"\\u7a7a\\u624b\\u9053\",\n",
       "                \"\\u7ade\\u6280\\u4f53\\u64cd\",\n",
       "                \"\\u7fbd\\u6bdb\\u7403\",\n",
       "                \"\\u81ea\\u7531\\u5f0f\\u6454\\u8de4\",\n",
       "                \"\\u82b1\\u6837\\u6e38\\u6cf3\",\n",
       "                \"\\u8d5b\\u8247\",\n",
       "                \"\\u8dc6\\u62f3\\u9053\",\n",
       "                \"\\u8df3\\u6c34\",\n",
       "                \"\\u8e66\\u5e8a\\u4f53\\u64cd\",\n",
       "                \"\\u9759\\u6c34\\u76ae\\u5212\\u8247\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"left\": \"2%\",\n",
       "            \"top\": \"15%\",\n",
       "            \"orient\": \"vertical\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u4e2d\\u56fd\\u961f\\u5956\\u724c\\u5206\\u5e03\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_2d4e35a86559481cbf6018915ed78d7d.setOption(option_2d4e35a86559481cbf6018915ed78d7d);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x7fd2f0997c88>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Pie\n",
    "\n",
    "c = (\n",
    "    Pie(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))\n",
    "    .add(\n",
    "        \"\",\n",
    "        [list(z) for z in zip(xiangmu, jiangpai)],\n",
    "        radius=[\"40%\", \"75%\"],\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"中国队奖牌分布\"),\n",
    "        legend_opts=opts.LegendOpts(orient=\"vertical\", pos_top=\"15%\", pos_left=\"2%\"),\n",
    "    )\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\n",
    "    .render_notebook()\n",
    ")\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 21 - 地图\n",
    "\n",
    "绘制各国奖牌分布热力地图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'json' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/28/t7zydhln6l5g741hjy399cjm0000gn/T/ipykernel_23684/4269217332.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'总分'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'name_map.json'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'utf8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'json' is not defined"
     ]
    }
   ],
   "source": [
    "countrys = list(df1['国家奥委会'])\n",
    "data = list(df1['总分'])\n",
    "with open('name_map.json','r',encoding='utf8')as fp:\n",
    "    name = json.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'name' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/28/t7zydhln6l5g741hjy399cjm0000gn/T/ipykernel_23684/2332718087.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m c = (\n\u001b[1;32m      6\u001b[0m     \u001b[0mMap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mz\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcountrys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"world\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mis_map_symbol_show\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0;34m.\u001b[0m\u001b[0mset_series_opts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_opts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLabelOpts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_show\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     .set_global_opts(\n",
      "\u001b[0;31mNameError\u001b[0m: name 'name' is not defined"
     ]
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Map\n",
    "from pyecharts.faker import Faker\n",
    "\n",
    "c = (\n",
    "    Map()\n",
    "    .add(\"\", [list(z) for z in zip(countrys, data)], \"world\",is_map_symbol_show=False, name_map=name)\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"东京奥运会奖牌分布\"),\n",
    "        visualmap_opts=opts.VisualMapOpts(max_=200),\n",
    "    )\n",
    "    .render_notebook()\n",
    ")\n",
    "\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  22 - 动态图\n",
    "\n",
    "将排行榜前十名的奖牌变化动态展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! cd bar_chart_race-master;python setup.py install"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bar_chart_race as bcr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.pivot_table(df2,values = ['奖牌类型'],index = ['获奖时间','国家'],aggfunc = 'count').query(\"国家 == ['美国', '中国', '日本', '英国', 'ROC', '澳大利亚', '荷兰', '法国', '德国', '意大利']\")\n",
    "data = data.unstack()\n",
    "data.columns = data.columns.get_level_values(1)\n",
    "data.columns.name = None\n",
    "data = data.cumsum()\n",
    "data = data.fillna(axis=0,method='ffill').fillna(0)\n",
    "data.columns = ['Russia', 'China','Italian', 'Japan', 'the Netherlands',' German ',' France ', 'Australia', 'US',' British ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_format(x):\n",
    "    x = x.replace('月','-')\n",
    "    x = x.replace('日','')\n",
    "    x = '2021-' + x\n",
    "    return x\n",
    "data = data.reset_index()\n",
    "data['获奖时间'] = data['获奖时间'].map(time_format)\n",
    "data['获奖时间'] = pd.to_datetime(data['获奖时间'])\n",
    "data = data.set_index('获奖时间')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'bcr' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/28/t7zydhln6l5g741hjy399cjm0000gn/T/ipykernel_23684/3825255761.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m bcr.bar_chart_race(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'h'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'desc'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mn_bars\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'bcr' is not defined"
     ]
    }
   ],
   "source": [
    "bcr.bar_chart_race(\n",
    "    df=data,\n",
    "    orientation='h',\n",
    "    sort='desc',\n",
    "    n_bars=10,\n",
    "    fixed_order=False,\n",
    "    fixed_max=True,\n",
    "    steps_per_period=30,\n",
    "    interpolate_period=False,\n",
    "    label_bars=True,\n",
    "    bar_size=.95,\n",
    "    period_label={'x': .99, 'y': .25, 'ha': 'right', 'va': 'center'},\n",
    "    period_fmt='%Y-%m-%d',\n",
    "    period_summary_func=lambda v, r: {'x': .99, 'y': .18,\n",
    "                                      's': f'Total MEDALS: {v.nlargest(6).sum():,.0f}',\n",
    "                                      'ha': 'right', 'size': 8, 'family': 'Courier New'},\n",
    "    perpendicular_bar_func='median',\n",
    "    period_length=500,\n",
    "    figsize=(5, 3),\n",
    "    dpi=160,\n",
    "    cmap='dark12',\n",
    "    title='Tokyo 2021 Medal changes',\n",
    "    title_size='',\n",
    "    bar_label_size=7,\n",
    "    tick_label_size=7,\n",
    "    #shared_fontdict={'family': 'Courier New', 'color': 'rebeccapurple'},\n",
    "    scale='linear',\n",
    "    writer=None,\n",
    "    fig=None,\n",
    "    bar_kwargs={'alpha': .7},\n",
    "    filter_column_colors=False)  "
   ]
  }
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